The present invention relates to systems and methods for optimally pricing high-volume commercial transactions between businesses, referred to as business-to-business (B2B) pricing.
For example, consider a negotiation between a supplier of raw materials (the seller) and a manufacturer (the buyer). Abstracting away the details of the negotiation, it may be reduced to a final price offer named by the seller. If the price is rejected, the seller incurs a high opportunity cost (lost revenue); however, it may not be clear whether a lower offer would have gotten the deal, and if so, how much lower it should have been. If the price is accepted, the seller is left wondering whether a higher price would have also worked. The seller makes many such pricing decisions over time, and attempts to maximize revenue, subject to considerable uncertainty about buyer behavior and willingness to pay.
Looking at historical pricing information, common trends are identified in these B2B pricing scenarios. These challenges include:
Big Data.
The data is highly heterogeneous, covering thousands of distinct products and buyers. Different product types have different price sensitivities. Consequently, the data contain a large number of “rows” (observed deals) as well as “columns” (explanatory variables). Predictive models may thus be vulnerable to noise accumulation, spurious correlations, and computational issues.
Noise.
Often the data is restricted to a binary (yes/no) response from the buyer, representing whether the seller's price was accepted or rejected. The proportion of accepted offers (“wins”) is very low. Furthermore, many of the products and buyers may appear infrequently and have few or no wins. Even with a large amount of data, predictive models are likely to be inaccurate.
High Cost of Failure.
If a price is rejected, the seller's revenue is zero. In B2B transactions, the total value of the deal may be in the millions of dollars. If the historical data are insufficient to make accurate predictions about future deals, the seller must learn quickly from new deals as they take place. It is thus not enough to use a pricing strategy that works well “over the long run,” as the practical value is in the very short term.
It is therefore apparent that an urgent need exists for systems and methods for using predictive and prescriptive analytics (statistical modeling and price optimization) in B2B pricing. In addition to short-term performance, computational efficiency is also an issue. Ideally, price optimization should be implementable in real time and on demand, so that a sales representative may access it during a negotiation through a tablet app.